
"""simple docstring"""


import os

import numpy
import onnx



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		a.name
       UpperCamelCase       			=		b.name

       UpperCamelCase       			=		""
       UpperCamelCase       			=		""

       UpperCamelCase       			=		a == b

       UpperCamelCase       			=		name_a
       UpperCamelCase       			=		name_b

       return res



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       for i, input_name in enumerate(node_proto.input					):
              if input_name == name:
                     node_proto.input.insert(UpperCAmelCase_		,   UpperCAmelCase_					)
                     node_proto.input.pop(i + 1					)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              _graph_replace_input_with(node_proto.attribute[1].g		,   UpperCAmelCase_		,   UpperCAmelCase_					)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g		,   UpperCAmelCase_		,   UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				str:







       """simple docstring"""
       for n in graph_proto.node:
              _node_replace_input_with(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		list(model.graph.initializer					)
       UpperCamelCase       			=		list(model_without_ext.graph.initializer					)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              UpperCamelCase       			=		inits[i].name
              UpperCamelCase       			=		inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]					)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph		,   UpperCAmelCase_		,   UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.basename(UpperCAmelCase_					)

       UpperCamelCase       			=		onnx.load(os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					)					)

       UpperCamelCase       			=		list(model.graph.initializer					)

       UpperCamelCase       			=		set()
       UpperCamelCase       			=		{}
       UpperCamelCase       			=		[]

       UpperCamelCase       			=		0

       for i in range(len(UpperCAmelCase_					)					):
              if i in dup_set:
                     continue

              for j in range(i + 1		,   len(UpperCAmelCase_					)					):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]		,   inits[j]					):
                            dup_set.add(UpperCAmelCase_					)
                            dup_set.add(UpperCAmelCase_					)

                            UpperCamelCase       			=		inits[j].data_type
                            UpperCamelCase       			=		numpy.prod(inits[j].dims					)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("unexpected data type: "		,   UpperCAmelCase_					)
                            total_reduced_size += mem_size

                            UpperCamelCase       			=		inits[i].name
                            UpperCamelCase       			=		inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(UpperCAmelCase_					)
                            else:
                                   UpperCamelCase       			=		[name_j]
                            ind_to_replace.append((j, i)					)

       print("total reduced size: "		,   total_reduced_size / 10_24 / 10_24 / 10_24		,   "GB"					)

       UpperCamelCase       			=		sorted(UpperCAmelCase_					)
       _remove_dup_initializers_from_model(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		"optimized_" + model_file_name
       UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					)
       onnx.save(UpperCAmelCase_		,   UpperCAmelCase_					)

       return new_model







"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





